Module code: MANM529

Module Overview

This module is designed to introduce to students how mathematical and econometric methods can be used to model diverse transformation processes to establish benchmarks of efficiency and productivity for organisations, and how to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience of implementing an efficiency and productivity assessment with real case studies using specialised software.

Module provider

Surrey Business School

Module Leader


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 90

Lecture Hours: 11

Seminar Hours: 4

Laboratory Hours: 22

Guided Learning: 12

Captured Content: 11

Module Availability

Semester 2

Prerequisites / Co-requisites

MANM304 Operational Analytics

Module content

  • Introduction to the notions of comparative performance measurement, unit of assessment, measures of efficiency.

  • Introduction and basics of Data Envelopment Analysis (DEA), formulation and graphical presentation

  • Extensions of the basic DEA models considering variable returns to scale

  • Performance evaluation over time, Malmquist productivity Index

  • A case study of evaluation of efficiency and productivity of financial institution / banking industry

  • Performance evaluation of two / three stage production process

  • Network DEA

  • A case study of evaluation of efficiency and productivity of healthcare centres / hospitals

  • Review of other methods including regression based methods for comparative efficiency assessments.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Group assignment 60
Examination Online Online (Open Book) Examination within 4 hour window (2 hours) 40

Alternative Assessment

Students will undertake an individual version of the coursework.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they:

  • Understand iDEA models for decision-making by applying appropriate model to each dataset 

  • Can advice decision makers for appropriate action to increase the efficiency and / or productivity of the business;

  • Demonstrate abilities of presenting solutions to business managers.

Thus, the summative assessment for this module consists of:

  • Coursework Group Assignment: 60%

    • The coursework student's to demonstrate the ability to follow the interpretation of the results from end-to-end, to take a real-world business problem and produce realistic, robust and understandable options and insights. This combines the technical skills above with the softer skills of process and communication that transform raw data into actionable, decision-making insights.

  • Open Book Exam: 40%

    • The exam enables student's to demonstrate technical skills through a series of mathematical and data questions (e.g. testing the student¿s ability to calculate and extract efficiency score and work out key outcomes from the model such as targets, peers, weights, etc.). These technical skills are arguably the primary objective for the business analytics consultant and of employers, and so it is necessary to specifically test the student's mathematical capability within an exam setting.

Formative assessment

Students will be given real dataset to analyse in the computer labs. Additional tasks at the end of the lab sheets will form the basis of their independent study. The solutions provided to the lab sheets (including the additional tasks) will form the formative assessments, as students can assess their own work and can also use the labs and student feedback and consultation hours for advice and feedback.


One of the final face-to-face sessions will be reserved to discuss assessment strategies and give feedback on the module as a whole.

Module aims

  • Enable students to understand how analytical methods such as Data Envelopment Analysis (DEA) can be used to derive benchmarks, targets and measures of efficiency and productivity for decision maker units
  • Understand theory of performance evaluation and apply them in complex multi-output multi-input contexts in the production of goods and services.
  • Provide students with hands-on experience on implementing an efficiency and productivity assessment using these methods with the help of specialised software.

Learning outcomes

Attributes Developed
001 Design and implement comparative efficiency assessment using non-parametric approaches such as Data Envelopment Analysis, convincingly justifying their choices in model creation and approach selection based on relevant literature, the data available and evidence from the analysis CKT
002 Formulate and interpret the output of an efficiency/productivity analysis and evaluate its meaning and significance CK
003 Identify and discuss any issues they might come across during the assessment exercise and provide plausible explanations/reasons for their findings C
004 Use specialist software, namely PIM-DEA or similar software to carry out comparative efficiency assessment PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to cultivate an understanding of the Analytics for comparative assessment of decision making units. Students will be taught with each week allowing students to learn how to apply the theoretical concept they learned to example of real datasets in different context.
The learning and teaching methods include:
o Synthesising theories of data envelopment analysis and related areas;
o Hands-on-approach by evaluating several software tools relevant to measuring efficiency and productivity of decision making units;
o Demonstrating evidence of background reading and research of the academic and practitioner literature relevant to performance evaluation of homogonies decision making units.

This module is delivered as a mix of lectures and lab classes. Web-based learning support and electronic resources will be provided.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: MANM529

Other information

Employability: The module is highly applied and directly teaches key skills needed for Business Analytics graduates going into analytical careers.

Digital capabilities: Students develop digital capabilities specifically by using problem-solving and analytical skills to use data to make decisions how to transform data into useful insights by understanding how data gets turned into knowledge and eventually into insight.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Analytics MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.